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Record W2971761547 · doi:10.1115/1.4044540

The Modeling of Redundantly Actuated Mechanical Systems

2019· article· en· W2971761547 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2019
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
FundersDepartment of Education of Zhejiang ProvinceChina Scholarship CouncilNatural Science Foundation of Zhejiang ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsActuatorControl theory (sociology)TorqueWrenchMechanical systemMoore–Penrose pseudoinverseKinematicsComputer scienceScrew theoryInverseControl engineeringMathematicsEngineeringMechanical engineeringControl (management)RobotArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Dynamics modeling is essential in the design and control of mechanical systems, the focus of the paper being redundantly actuated systems, which bring about special challenges. The authors resort to the natural orthogonal complement (NOC), based on an adaptation of screw theory, to derive the dynamics model. Benefiting from the elimination of the constraint wrenches, the NOC offers a simple, systematic alternative to the modeling of redundantly actuated mechanical systems. The optimum actuator-torque distribution is determined via Euclidean-norm minimization; then, by relying on the QR-decomposition, an efficient and robust method is produced to compute explicitly the right Moore–Penrose generalized inverse of the coefficient matrix. The methodology is illustrated via a case study involving a redundantly actuated parallel-kinematics machine with three degrees of freedom and four actuators.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.737
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.192
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it